Overview

Dataset statistics

Number of variables11
Number of observations250
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.6 KiB
Average record size in memory88.5 B

Variable types

NUM11

Reproduction

Analysis started2020-08-25 00:44:24.387419
Analysis finished2020-08-25 00:44:41.473615
Duration17.09 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

oz1 has unique values Unique
oz2 has unique values Unique
oz3 has unique values Unique
oz4 has unique values Unique
oz5 has unique values Unique
oz6 has unique values Unique
oz7 has unique values Unique
oz8 has unique values Unique
oz9 has unique values Unique
oz10 has unique values Unique
target has unique values Unique

Variables

oz1
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.539025783538818e-09
Minimum-1.7356771230697632
Maximum1.7483699321746826
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:44:41.519756image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.735677123
5-th percentile-1.519907719
Q1-0.8768334538
median0.004678466357
Q30.8438749909
95-th percentile1.625628453
Maximum1.748369932
Range3.484047055
Interquartile range (IQR)1.720708445

Descriptive statistics

Standard deviation0.9999999961
Coefficient of variation (CV)282563636.8
Kurtosis-1.160094438
Mean3.539025784e-09
Median Absolute Deviation (MAD)0.8595966101
Skewness0.06838413412
Sum8.847564459e-07
Variance0.9999999921
2020-08-25T00:44:41.623412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.84482026110.4%
 
1.01788735410.4%
 
1.58531975710.4%
 
0.715511858510.4%
 
-0.394376397110.4%
 
0.757009029410.4%
 
1.66147518210.4%
 
1.37974691410.4%
 
0.245354384210.4%
 
-1.73567712310.4%
 
1.74251115310.4%
 
-0.960121750810.4%
 
1.35352444610.4%
 
-0.888613820110.4%
 
-1.21417844310.4%
 
-1.53253078510.4%
 
-0.8067665110.4%
 
0.323805034210.4%
 
0.494555443510.4%
 
1.36552774910.4%
 
1.533493410.4%
 
-1.3381793510.4%
 
-0.339939296210.4%
 
-0.735494196410.4%
 
-0.104753769910.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.73567712310.4%
 
-1.69374895110.4%
 
-1.68518519410.4%
 
-1.67791652710.4%
 
-1.63588726510.4%
 
-1.62282884110.4%
 
-1.61454880210.4%
 
-1.61175835110.4%
 
-1.61129522310.4%
 
-1.60835599910.4%
 
ValueCountFrequency (%) 
1.74836993210.4%
 
1.74251115310.4%
 
1.70302104910.4%
 
1.68730485410.4%
 
1.66147518210.4%
 
1.66124904210.4%
 
1.65958595310.4%
 
1.65505611910.4%
 
1.65027320410.4%
 
1.64869689910.4%
 

oz2
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.1646188813330128e-09
Minimum-1.756024718284607
Maximum1.687329649925232
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:44:41.737809image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.756024718
5-th percentile-1.569909126
Q1-0.8275365978
median-0.07865515724
Q30.8497955948
95-th percentile1.594103599
Maximum1.68732965
Range3.443354368
Interquartile range (IQR)1.677332193

Descriptive statistics

Standard deviation0.999999998
Coefficient of variation (CV)-858649996.2
Kurtosis-1.131694573
Mean-1.164618881e-09
Median Absolute Deviation (MAD)0.8374443054
Skewness0.06246210773
Sum-2.911547203e-07
Variance0.999999996
2020-08-25T00:44:41.839590image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.60644447810.4%
 
-1.17904770410.4%
 
0.328954607210.4%
 
1.45860195210.4%
 
0.805751800510.4%
 
-0.473483353910.4%
 
1.06482601210.4%
 
-0.275821834810.4%
 
-0.567566156410.4%
 
1.13903415210.4%
 
0.34432697310.4%
 
1.14000034310.4%
 
-0.470018148410.4%
 
-0.427402973210.4%
 
-1.34799671210.4%
 
-1.57943606410.4%
 
-0.489327430710.4%
 
-0.745770573610.4%
 
-1.1888010510.4%
 
1.16955685610.4%
 
-0.592934131610.4%
 
-0.990879118410.4%
 
0.320722937610.4%
 
0.779939889910.4%
 
1.61488091910.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.75602471810.4%
 
-1.75537395510.4%
 
-1.75405824210.4%
 
-1.73515152910.4%
 
-1.72420585210.4%
 
-1.71432936210.4%
 
-1.6959260710.4%
 
-1.69234943410.4%
 
-1.64564645310.4%
 
-1.61256730610.4%
 
ValueCountFrequency (%) 
1.6873296510.4%
 
1.68543291110.4%
 
1.68277335210.4%
 
1.67846524710.4%
 
1.65999233710.4%
 
1.65781915210.4%
 
1.64946854110.4%
 
1.62079274710.4%
 
1.61488091910.4%
 
1.61293768910.4%
 

oz3
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.2668391501667883e-09
Minimum-1.786012887954712
Maximum1.6854583024978638
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:44:41.959237image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.786012888
5-th percentile-1.612475973
Q1-0.7930390984
median-0.04328698851
Q30.8518139273
95-th percentile1.559239823
Maximum1.685458302
Range3.47147119
Interquartile range (IQR)1.644853026

Descriptive statistics

Standard deviation1.000000004
Coefficient of variation (CV)-441142903.4
Kurtosis-1.091418936
Mean-2.26683915e-09
Median Absolute Deviation (MAD)0.8000595868
Skewness-0.03516477793
Sum-5.667097875e-07
Variance1.000000009
2020-08-25T00:44:42.064785image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.803220868110.4%
 
-1.47005724910.4%
 
-0.376055806910.4%
 
0.314793735710.4%
 
-1.20860135610.4%
 
1.23562085610.4%
 
1.50250327610.4%
 
-1.73572409210.4%
 
1.13714575810.4%
 
-0.498802512910.4%
 
-1.11950051810.4%
 
0.780938327310.4%
 
1.34116959610.4%
 
-0.652511298710.4%
 
-1.16536903410.4%
 
0.974770486410.4%
 
1.62136805110.4%
 
0.347247719810.4%
 
1.57062983510.4%
 
1.19011449810.4%
 
0.27620142710.4%
 
1.62628710310.4%
 
-0.395096063610.4%
 
1.47588825210.4%
 
-0.0557344667610.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.78601288810.4%
 
-1.78552782510.4%
 
-1.76346659710.4%
 
-1.73572409210.4%
 
-1.72277140610.4%
 
-1.71483385610.4%
 
-1.67503035110.4%
 
-1.65832555310.4%
 
-1.65704560310.4%
 
-1.65235686310.4%
 
ValueCountFrequency (%) 
1.68545830210.4%
 
1.67955553510.4%
 
1.6684571510.4%
 
1.66791117210.4%
 
1.62730753410.4%
 
1.62628710310.4%
 
1.62136805110.4%
 
1.598426710.4%
 
1.59819912910.4%
 
1.59278047110.4%
 

oz4
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.648501873016358e-10
Minimum-1.5731245279312134
Maximum1.7560973167419434
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:44:42.182238image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.573124528
5-th percentile-1.47759338
Q1-0.884022519
median0.006939318497
Q30.8553666174
95-th percentile1.621815598
Maximum1.756097317
Range3.329221845
Interquartile range (IQR)1.739389136

Descriptive statistics

Standard deviation0.9999999975
Coefficient of variation (CV)1036430329
Kurtosis-1.186663963
Mean9.648501873e-10
Median Absolute Deviation (MAD)0.8727717069
Skewness0.1254368201
Sum2.412125468e-07
Variance0.9999999949
2020-08-25T00:44:42.287789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.00834654457910.4%
 
-0.416575223210.4%
 
0.899097025410.4%
 
-0.563645720510.4%
 
0.131590306810.4%
 
1.01693344110.4%
 
0.532390773310.4%
 
0.23551385110.4%
 
1.09602010310.4%
 
0.647078633310.4%
 
-0.360182523710.4%
 
-0.663722753510.4%
 
-1.23212897810.4%
 
-1.05693101910.4%
 
-1.4426696310.4%
 
-1.55440700110.4%
 
-0.592104971410.4%
 
-0.432349979910.4%
 
-0.239414036310.4%
 
-1.46218252210.4%
 
0.779918491810.4%
 
-1.20308744910.4%
 
-1.43287193810.4%
 
-0.503054201610.4%
 
0.762329101610.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.57312452810.4%
 
-1.57087266410.4%
 
-1.55561816710.4%
 
-1.55440700110.4%
 
-1.5464227210.4%
 
-1.54603445510.4%
 
-1.54361021510.4%
 
-1.53714740310.4%
 
-1.52575147210.4%
 
-1.49754834210.4%
 
ValueCountFrequency (%) 
1.75609731710.4%
 
1.75115430410.4%
 
1.7451077710.4%
 
1.73279510.4%
 
1.73231244110.4%
 
1.72527480110.4%
 
1.72106444810.4%
 
1.69709277210.4%
 
1.69118833510.4%
 
1.65418279210.4%
 

oz5
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.377216100692749e-11
Minimum-1.9158393144607544
Maximum1.6581294536590576
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:44:42.407874image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.915839314
5-th percentile-1.67609154
Q1-0.826113075
median0.03825314343
Q30.7908568382
95-th percentile1.483461297
Maximum1.658129454
Range3.573968768
Interquartile range (IQR)1.616969913

Descriptive statistics

Standard deviation0.9999999989
Coefficient of variation (CV)-2.28455707e+10
Kurtosis-1.06845508
Mean-4.377216101e-11
Median Absolute Deviation (MAD)0.7962214351
Skewness-0.1569357465
Sum-1.094304025e-08
Variance0.9999999979
2020-08-25T00:44:42.510994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.443017065510.4%
 
-0.903885185710.4%
 
0.927091717710.4%
 
1.0709426410.4%
 
1.26499295210.4%
 
0.982577860410.4%
 
1.20638704310.4%
 
-1.78059804410.4%
 
0.684148192410.4%
 
0.596836268910.4%
 
0.158468455110.4%
 
-1.33526825910.4%
 
0.468582361910.4%
 
-1.13995218310.4%
 
-1.16045594210.4%
 
-0.545557320110.4%
 
-0.207670703510.4%
 
0.505485653910.4%
 
0.92250579610.4%
 
0.358712494410.4%
 
0.490186572110.4%
 
-0.174717053810.4%
 
1.56146645510.4%
 
1.58132255110.4%
 
0.694469213510.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.91583931410.4%
 
-1.88095104710.4%
 
-1.87986099710.4%
 
-1.85641920610.4%
 
-1.81397187710.4%
 
-1.80656933810.4%
 
-1.78059804410.4%
 
-1.74652767210.4%
 
-1.72238504910.4%
 
-1.71210753910.4%
 
ValueCountFrequency (%) 
1.65812945410.4%
 
1.65125215110.4%
 
1.64939534710.4%
 
1.64845228210.4%
 
1.64205944510.4%
 
1.64100468210.4%
 
1.59293675410.4%
 
1.58132255110.4%
 
1.56146645510.4%
 
1.54738175910.4%
 

oz6
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5297049432992936e-10
Minimum-1.8011727333068848
Maximum1.726836085319519
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:44:42.629068image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.801172733
5-th percentile-1.558025652
Q1-0.8419548273
median0.06350662
Q30.7798946649
95-th percentile1.529253316
Maximum1.726836085
Range3.528008819
Interquartile range (IQR)1.621849492

Descriptive statistics

Standard deviation0.9999999985
Coefficient of variation (CV)3953030179
Kurtosis-1.14486453
Mean2.529704943e-10
Median Absolute Deviation (MAD)0.7873868048
Skewness-0.06050243016
Sum6.324262358e-08
Variance0.999999997
2020-08-25T00:44:42.732664image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.70995104310.4%
 
-0.17996807410.4%
 
0.7574989210.4%
 
0.700475275510.4%
 
0.492758929710.4%
 
-0.818520724810.4%
 
1.38152372810.4%
 
0.474275201610.4%
 
-0.0618689432710.4%
 
-0.171791717410.4%
 
-1.52765381310.4%
 
0.0680734589710.4%
 
0.367599636310.4%
 
1.45380115510.4%
 
0.497632801510.4%
 
1.28641605410.4%
 
0.299127012510.4%
 
-0.00043971286510.4%
 
1.21121382710.4%
 
-0.261298745910.4%
 
-0.219571262610.4%
 
-0.0458150096210.4%
 
1.15166914510.4%
 
0.415834248110.4%
 
-0.0783845856810.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.80117273310.4%
 
-1.77014362810.4%
 
-1.74411368410.4%
 
-1.73067438610.4%
 
-1.7080880410.4%
 
-1.70505785910.4%
 
-1.67948424810.4%
 
-1.64004874210.4%
 
-1.63176763110.4%
 
-1.61148679310.4%
 
ValueCountFrequency (%) 
1.72683608510.4%
 
1.72400295710.4%
 
1.70995104310.4%
 
1.65344417110.4%
 
1.65055489510.4%
 
1.6010408410.4%
 
1.58264756210.4%
 
1.58218193110.4%
 
1.55561304110.4%
 
1.54270052910.4%
 

oz7
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.241002559661865e-10
Minimum-1.803480863571167
Maximum1.650054931640625
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:44:42.847220image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.803480864
5-th percentile-1.53547731
Q1-0.8358773738
median-0.06548275799
Q30.9478500336
95-th percentile1.515449321
Maximum1.650054932
Range3.453535795
Interquartile range (IQR)1.783727407

Descriptive statistics

Standard deviation1.000000003
Coefficient of variation (CV)3085465021
Kurtosis-1.224243239
Mean3.24100256e-10
Median Absolute Deviation (MAD)0.8930355906
Skewness-0.008841905172
Sum8.102506399e-08
Variance1.000000006
2020-08-25T00:44:42.956082image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.0866087302610.4%
 
-0.902181446610.4%
 
-1.56967139210.4%
 
1.36361193710.4%
 
-0.160075157910.4%
 
-1.45247316410.4%
 
1.05403447210.4%
 
0.385578215110.4%
 
-0.163003504310.4%
 
-0.203164190110.4%
 
0.835603177510.4%
 
-0.383399248110.4%
 
-0.812164008610.4%
 
-1.53643083610.4%
 
-1.15446019210.4%
 
-0.464914679510.4%
 
-0.61442130810.4%
 
0.205235287510.4%
 
-0.827497124710.4%
 
-0.438551157710.4%
 
-0.551117837410.4%
 
-0.393621176510.4%
 
0.590463042310.4%
 
0.026203395810.4%
 
-1.41334450210.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.80348086410.4%
 
-1.80347490310.4%
 
-1.78500366210.4%
 
-1.73229956610.4%
 
-1.67595374610.4%
 
-1.67140948810.4%
 
-1.62934005310.4%
 
-1.6131968510.4%
 
-1.60851061310.4%
 
-1.56967139210.4%
 
ValueCountFrequency (%) 
1.65005493210.4%
 
1.64349365210.4%
 
1.63288760210.4%
 
1.63186335610.4%
 
1.62651944210.4%
 
1.61902117710.4%
 
1.61392629110.4%
 
1.58682775510.4%
 
1.5492675310.4%
 
1.54020643210.4%
 

oz8
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.227063143488976e-10
Minimum-1.7067319154739382
Maximum1.760989546775818
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:44:43.073876image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.706731915
5-th percentile-1.528315794
Q1-0.8550721854
median0.005011818837
Q30.8192818016
95-th percentile1.584375405
Maximum1.760989547
Range3.467721462
Interquartile range (IQR)1.674353987

Descriptive statistics

Standard deviation0.9999999978
Coefficient of variation (CV)-1383687921
Kurtosis-1.183495065
Mean-7.227063143e-10
Median Absolute Deviation (MAD)0.8373626689
Skewness0.04087398852
Sum-1.806765786e-07
Variance0.9999999956
2020-08-25T00:44:43.342487image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.29687762310.4%
 
0.799066603210.4%
 
0.877119541210.4%
 
-1.44526600810.4%
 
-0.179117754110.4%
 
-1.47923219210.4%
 
1.61946082110.4%
 
0.378740310710.4%
 
1.59015524410.4%
 
1.70976138110.4%
 
-0.318191200510.4%
 
-1.42217493110.4%
 
-1.32451593910.4%
 
-0.469257563410.4%
 
-1.09300649210.4%
 
0.261058598810.4%
 
-0.0774686783610.4%
 
-1.30692601210.4%
 
-1.05204153110.4%
 
0.198085188910.4%
 
0.980116963410.4%
 
-1.63057005410.4%
 
-1.52272820510.4%
 
0.554818332210.4%
 
0.71293193110.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.70673191510.4%
 
-1.68153047610.4%
 
-1.66346263910.4%
 
-1.64099013810.4%
 
-1.63057005410.4%
 
-1.61232578810.4%
 
-1.59607946910.4%
 
-1.59166324110.4%
 
-1.58468711410.4%
 
-1.58446288110.4%
 
ValueCountFrequency (%) 
1.76098954710.4%
 
1.75605833510.4%
 
1.73091554610.4%
 
1.72458040710.4%
 
1.71696245710.4%
 
1.70976138110.4%
 
1.70769619910.4%
 
1.66644489810.4%
 
1.66071534210.4%
 
1.64730572710.4%
 

oz9
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.670597940683365e-10
Minimum-1.7313770055770874
Maximum1.697670340538025
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:44:43.461423image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.731377006
5-th percentile-1.556443679
Q1-0.9487165362
median0.1966383234
Q30.8341069967
95-th percentile1.488619018
Maximum1.697670341
Range3.429047346
Interquartile range (IQR)1.782823533

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)1499115985
Kurtosis-1.264217111
Mean6.670597941e-10
Median Absolute Deviation (MAD)0.8739037514
Skewness-0.1232420542
Sum1.667649485e-07
Variance1.000000001
2020-08-25T00:44:43.565858image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.45574629310.4%
 
-1.64486861210.4%
 
0.381444811810.4%
 
-1.54554295510.4%
 
0.236375331910.4%
 
0.741406023510.4%
 
0.518334507910.4%
 
0.741396367510.4%
 
0.248703241310.4%
 
-1.42712485810.4%
 
0.590025484610.4%
 
-0.121518947210.4%
 
-0.920585036310.4%
 
1.44594883910.4%
 
-1.56576752710.4%
 
-0.783368587510.4%
 
0.338934302310.4%
 
1.2415362610.4%
 
1.0823553810.4%
 
0.802894055810.4%
 
-1.02472448310.4%
 
1.05438017810.4%
 
0.741290152110.4%
 
0.694974720510.4%
 
-0.0811949521310.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.73137700610.4%
 
-1.71429836810.4%
 
-1.71136522310.4%
 
-1.69817709910.4%
 
-1.67150318610.4%
 
-1.66612136410.4%
 
-1.64486861210.4%
 
-1.63993024810.4%
 
-1.63681912410.4%
 
-1.6342884310.4%
 
ValueCountFrequency (%) 
1.69767034110.4%
 
1.68795573710.4%
 
1.66333377410.4%
 
1.64838910110.4%
 
1.63719606410.4%
 
1.5754625810.4%
 
1.55118823110.4%
 
1.53968095810.4%
 
1.50505304310.4%
 
1.50376057610.4%
 

oz10
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.266373485326767e-09
Minimum-1.7601324319839478
Maximum1.7235318422317505
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:44:43.682002image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.760132432
5-th percentile-1.652176684
Q1-0.7267832458
median-0.0266257422
Q30.8308644593
95-th percentile1.517282522
Maximum1.723531842
Range3.483664274
Interquartile range (IQR)1.557647705

Descriptive statistics

Standard deviation0.999999997
Coefficient of variation (CV)-441233540.5
Kurtosis-1.061409615
Mean-2.266373485e-09
Median Absolute Deviation (MAD)0.796880235
Skewness-0.06774946316
Sum-5.665933713e-07
Variance0.999999994
2020-08-25T00:44:43.794475image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.731933116910.4%
 
1.37730324310.4%
 
-1.23379385510.4%
 
1.59804773310.4%
 
0.294530600310.4%
 
0.449069440410.4%
 
-0.700151205110.4%
 
-1.67696964710.4%
 
1.30700933910.4%
 
-1.76013243210.4%
 
-1.08571815510.4%
 
0.425128996410.4%
 
0.68572658310.4%
 
1.32750201210.4%
 
1.26207268210.4%
 
-1.75062847110.4%
 
-0.553600311310.4%
 
-1.33433079710.4%
 
-0.0265396218710.4%
 
0.317224055510.4%
 
-0.280358105910.4%
 
1.4768202310.4%
 
1.59603571910.4%
 
0.872724413910.4%
 
0.417886793610.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.76013243210.4%
 
-1.75996470510.4%
 
-1.75062847110.4%
 
-1.71744203610.4%
 
-1.70812952510.4%
 
-1.7052913910.4%
 
-1.70413231810.4%
 
-1.68876373810.4%
 
-1.68673753710.4%
 
-1.67696964710.4%
 
ValueCountFrequency (%) 
1.72353184210.4%
 
1.69092667110.4%
 
1.68873548510.4%
 
1.67469596910.4%
 
1.65600919710.4%
 
1.59804773310.4%
 
1.59603571910.4%
 
1.59276354310.4%
 
1.59252595910.4%
 
1.57952725910.4%
 

target
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.2840838432312014e-10
Minimum-2.6574044227600098
Maximum2.0781395435333248
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:44:43.923848image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.657404423
5-th percentile-1.666684681
Q1-0.603786692
median0.005238275509
Q30.6472773552
95-th percentile1.660040593
Maximum2.078139544
Range4.735543966
Interquartile range (IQR)1.251064047

Descriptive statistics

Standard deviation0.9999999999
Coefficient of variation (CV)-2334221356
Kurtosis-0.4953934849
Mean-4.284083843e-10
Median Absolute Deviation (MAD)0.6324464786
Skewness-0.07245626626
Sum-1.071020961e-07
Variance0.9999999998
2020-08-25T00:44:44.022299image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.91845691210.4%
 
-0.405594050910.4%
 
0.511895179710.4%
 
-1.97035455710.4%
 
-0.711110830310.4%
 
0.594408929310.4%
 
1.33919978110.4%
 
0.751631557910.4%
 
-0.243120759710.4%
 
0.270834118110.4%
 
-1.59114694610.4%
 
-1.66900956610.4%
 
-1.42779660210.4%
 
0.519602239110.4%
 
1.44366884210.4%
 
-1.03350114810.4%
 
-0.0709369555110.4%
 
1.43584537510.4%
 
-1.54033350910.4%
 
1.8225543510.4%
 
1.68290162110.4%
 
-1.60086131110.4%
 
1.67312550510.4%
 
-0.235751688510.4%
 
0.907714247710.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-2.65740442310.4%
 
-2.28484296810.4%
 
-2.26697492610.4%
 
-2.22280716910.4%
 
-2.00137877510.4%
 
-1.97035455710.4%
 
-1.77592635210.4%
 
-1.77453184110.4%
 
-1.75899076510.4%
 
-1.7241427910.4%
 
ValueCountFrequency (%) 
2.07813954410.4%
 
2.0079150210.4%
 
1.94362211210.4%
 
1.88081133410.4%
 
1.84328484510.4%
 
1.82590091210.4%
 
1.8225543510.4%
 
1.80042660210.4%
 
1.71360576210.4%
 
1.68290162110.4%
 

Interactions

2020-08-25T00:44:24.821633image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:24.949329image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:25.078193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:25.206466image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:25.332692image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:25.464166image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:25.592885image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:25.718965image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:25.845973image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:25.972127image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:26.104641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:26.228728image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:26.353427image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:26.479627image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:26.612380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:26.741102image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:26.873161image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:27.001040image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:27.133457image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:27.274039image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:27.405244image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:27.531118image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:27.660923image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:27.787322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:27.915915image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:28.210460image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:28.336915image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:28.474838image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:28.605323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:28.733341image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:28.861565image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:28.989979image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:29.123545image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:29.244068image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:29.370941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:29.497388image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:29.631390image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:29.758873image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:29.891392image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:30.018039image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:30.148418image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:30.277741image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:30.409284image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:30.539829image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:30.663640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:30.796017image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:30.930120image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:31.063351image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:31.198581image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:31.332691image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:31.465467image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:31.597590image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:31.732294image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:31.864200image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:32.170308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:32.293930image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:32.420332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:32.545961image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:32.674158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:32.807377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:32.940379image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:33.070313image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:33.202974image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:33.331234image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:33.461729image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:33.589720image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:33.713402image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:33.841908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:33.969578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:34.109718image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:34.239469image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:34.368727image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:34.495319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:34.620633image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:34.749331image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:34.874635image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:35.001207image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:35.121380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:35.255890image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:35.383203image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:35.511831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:35.646784image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:35.782822image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:36.074709image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:36.202275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:36.341114image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:36.469159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:36.595514image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:36.714346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:36.844410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:36.974660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:37.103394image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:37.235452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:37.375010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:37.501434image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:37.628801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:37.756671image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:37.887909image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:38.016594image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:38.135885image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:38.261105image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:38.390918image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:38.518097image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:38.647443image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:38.778494image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:38.908621image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:39.036335image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:39.164538image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:39.292417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:39.422945image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:39.541557image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:39.656118image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:39.938484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:40.055757image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:40.171990image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:40.291884image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:40.412336image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:40.529083image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:40.645792image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:40.761244image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:40.880287image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:44:44.141551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:44:44.362549image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:44:44.584899image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:44:44.805285image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:44:41.099761image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:44:41.365820image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
0-0.5115070.1318221.549419-1.287627-0.453457-0.5370500.8278131.0024101.404576-0.223567-0.556209
1-0.737975-1.735152-0.2235320.284592-1.2613080.4632441.538099-1.343731-0.916030-0.041255-1.651536
20.2453540.534813-0.1842631.121569-1.1397750.609074-0.1601390.6175440.9418151.1433180.751632
3-1.0921610.9839591.3900391.7323120.7031710.839446-0.991851-0.320751-1.289482-0.4517001.200406
41.4424640.041728-0.562306-1.546423-0.2354560.0680731.215540-0.924619-0.4115111.262073-0.489702
50.3955341.483520-1.455379-0.432350-0.863980-0.6393811.2639660.224089-0.715958-1.4978560.630205
6-0.518189-0.131319-0.0878281.003640-1.457650-1.5374240.689977-0.318191-1.3924110.447532-0.481378
70.341133-0.290273-0.6944360.9979750.2972771.582648-1.4133450.603210-0.9205850.5134830.495298
8-0.094463-0.0740990.535159-0.6381590.5292190.766896-0.569287-0.3646610.6949750.9907360.299339
90.331948-0.271279-1.3462681.5968811.342718-0.723499-0.5371700.2233030.2450631.5202041.943622

Last rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
240-0.168200-0.6197531.208912-0.8153790.5968360.2305251.0879811.551001-0.9124241.723532-0.778975
241-0.9142831.6494690.4549671.521971-0.1747171.5427011.1639311.2827870.2910691.5960360.907714
2421.659586-0.655256-0.172347-1.340694-1.1604560.1026150.127262-0.7129751.2577610.454136-1.073515
243-1.405969-1.557666-1.658326-0.551630-0.687453-1.0938601.089352-0.3799330.1356700.112471-0.828368
244-1.635887-0.538345-1.4700570.0466770.863418-1.1133050.6238950.4796820.5399630.152615-0.594748
2450.6248040.3139381.502503-1.573125-1.1395430.215232-1.2741921.6607150.7412901.4732400.224790
2460.846870-1.285915-0.1205500.5323911.1991930.446806-1.278770-1.554074-1.5455431.363560-0.104608
247-1.4885251.024207-0.6779561.458093-0.194421-0.464389-0.298485-0.7323920.371635-1.6867380.020308
2481.3631121.6148811.115120-0.9596681.2716091.5278271.503035-0.0063340.8827311.1764280.175283
249-1.1319430.462852-0.3763250.0499631.068573-1.4979500.086609-0.5580480.638933-1.483767-0.120641